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Human beings have encountered numerous natural disasters over time, andthe phenomenon of earthquake is one of the most destructive of them. In recent years, the complex behavior of earthquakes in terms of time and space has been investigated using complex networks, which has enabled us to know the global characteristics of this phenomenon. Studies on the earthquakes using the network method are based on how the network is constructed. In this paper, by constructing the network, the spatio-temporal recurrence of earthquakes which occurred in the Zagros region and were recorded at all broadband and short-period stations of the Iranian Seismological Center (ISC, http://irsc.ut.ac.ir) are subject to statistical review. The results showed that in the Zagros area the spatial recurrenceis about 0.020 km, and the time recurrence for all magnitudethresholds issimilar topower relationship (1/Tα) with α = -0.9. [ABSTRACT FROM AUTHOR]

In torts, proximate causation, or legal cause, examines whether harmful negligent conduct is "closely enough related" to the damages that ensue. Torts professors often use the metaphor of a stone being thrown into a pond to explain this rather amorphous legal doctrine. The ripples the stone creates surrounding it are the direct result of the act of it being thrown. The stone tossed into the pond, i.e., a negligent act, created an effect which perpetuated via ripples to a long distance, forever changing the entire pond, i.e., causing close and far damages. Can all of those affected by the negligent act be compensated? Should they? It is up to proximate causation to determine if a ripple is too remote from the thrown stone to be viewed as its "direct" or "foreseeable" result. However, this does not provide the legal system with a lot of guidance. This is where network theory can be helpful. Network theory holds great value when it comes to analyzing "accident networks" and the proximate nexus between the negligent conduct and the damages ensued. Network theory enables different stakeholders, such as regulators, judges, insurers, and policymakers, to visualize multifaceted legal scenarios in a manner that can assist them to better understand and evaluate the connections (edges) between the different entities (nodes) and their reciprocal relationships. In our case, the existence of a legal nexus between the negligent conduct and the harms thatfollowed. This Article offers to use network theory to enable the legal system to better justify and explain the results of their proximate causation tests via tangible measurements. Integrating network theory into the evaluation of proximate causation will ensure different stakeholders will better understand and internalize the value and importance of this important doctrine. It will provide a stronger basis for deciding that certain damages cannot be traced back to a negligent act as a matter of policy or fact. The core purpose of the proximate causation doctrine is to provide a "normative strainer" to prevent each and every one of us from being constantly liable for actions that perpetuated remotely from us. This Article aims to develop a network theory framework to better evaluate and implement this seminal doctrine. [ABSTRACT FROM AUTHOR]

The primary aim of statistical mechanics is to relate the micropscopic physics governed by Newton's laws, or else by quantum mechanics, to the macroscopic physics governed by the laws of thermodynamics. Macroscopic properties, such as temperature, may have no meaning at the microscopic level, but they arise as emergent properties in statistical mechanics when the number of particles becomes sufficiently large. The success of statistical mechanics in explaining and predicting a variety of macroscopic phenomena has motivated the application of concepts and methods of statistical physics to study complex systems and complex networks, including in neuroscience. Our aim here is to apply this approach to a key problem in neuroscience: how can we quantitatively understand the neural correlates of altered mental states induced by psychedelic substances? We first give an overview of basic concepts concerning functional connectivity of brain regions. We then discuss the phenomenology and the neuroscience related to psychedelics. Finally, we review some studies that have applied statistical physics and the theory of complex networks to try to gain a better understanding of the brain under the influence of psychedelics. [ABSTRACT FROM AUTHOR]

Chromatin communities stabilized by protein machinery play essential role in gene regulation and refine global polymeric folding of the chromatin fiber. However, treatment of these communities in the framework of the classical network theory (stochastic block model, SBM) does not take into account intrinsic linear connectivity of the chromatin loci. Here we propose the polymer block model, paving the way for community detection in polymer networks. On the basis of this new model we modify the non-backtracking flow operator and suggest the first protocol for annotation of compartmental domains in sparse single cell Hi-C matrices. In particular, we prove that our approach corresponds to the maximum entropy principle. The benchmark analyses demonstrates that the spectrum of the polymer non-backtracking operator resolves the true compartmental structure up to the theoretical detectability threshold, while all commonly used operators fail above it. We test various operators on real data and conclude that the sizes of the non-backtracking single cell domains are most close to the sizes of compartments from the population data. Moreover, the found domains clearly segregate in the gene density and correlate with the population compartmental mask, corroborating biological significance of our annotation of the chromatin compartmental domains in single cells Hi-C matrices. [ABSTRACT FROM AUTHOR]

The challenges faced by official statistics in the 21st century are manifold. We are surrounded by systems that are becoming substantially more complex. The emergence of new phenomena, such as globalisation, digitalisation, has increased the complexity of the reality that needs to be meaningfully and timeously captured by official statistics; it has resulted in the development of new patterns, routes, and types of data, giving us the opportunity to further improve the relevance of statistics. In response to these trends, we need to find new tools and methods for the measurement of these dynamic phenomena. Network theory is an innovative approach that can help us handle the complexity of the 21st century. However, so far it has not featured in mainstream official statistics. [ABSTRACT FROM AUTHOR]

Abstract Community detection algorithms have important significance in the research and practical application of complex network theory. This paper proposes a community detection method by improved label propagation and fuzzy C-means. Due to low accuracy and instability detection results, we modify original label propagation framework. Primarily, initial labels of vertexes are assigned by neighbor evaluation method. Secondarily, the labels of vertexes with large diversity in each community are revised by fuzzy C-means membership vectors. Tertiarily, parameters are updated until communities status is stabilized ultimately. The results showed that this method can achieve better accuracy on synthetic and real network. Highlights • Research on complex network community detection algorithm based on improved label propagation and fuzzy C-means. • The algorithm completes initialization classification of communities by neighbor evaluation method. • The algorithm adjusts the labels of unstable vertexes by fuzzy C-means membership vectors. • The accuracy of community detection is improved on synthetic and real network. [ABSTRACT FROM AUTHOR]

Abstract Community detection is an important task with great practical significance for understanding the structure and function of complex networks in various fields. As the real world networks become larger and more complex, it is a challenge to achieve high quality of community partitioning. In order to identify community structure more effectively in complex networks, a new algorithm, which iteratively deletes edges with restrictions is proposed in this paper. The algorithm first makes use of the connection strength between vertices to divide the original network into some strongly connected communities with optimal modularity by the improved edge-deleting process, and finally reconnects the isolated vertices to initial communities for optimizing community structure. Experiments on the real-world and synthetic networks prove that the proposed algorithm achieves a competitive performance compared with other reference algorithms. [ABSTRACT FROM AUTHOR]

Abstract We investigate the problem of locating the source of diffusion in complex networks without complete knowledge of nodes' states. Some currently known methods assume the information travels via a single, shortest path, which by assumption is the fastest way. We show that such a method leads to the overestimation of propagation time for synthetic and real networks, where multiple shortest paths as well as longer paths between vertices exist. We propose a new method of source estimation based on maximum likelihood principle, that takes into account existence multiple shortest paths. It shows up to 1.6 times higher accuracy in synthetic and real networks. Highlights • Multiple paths reduce time it takes to spread. • We find approximations for mean times and covariances as if paths or their links were equally probable. • These approximations result in 1.6 times higher accuracy than single path approximation. [ABSTRACT FROM AUTHOR]

Abstract We study the synchronization of Kuramoto oscillators on networks where only a fraction of them is subjected to a periodic external force. When all oscillators receive the external drive the system always synchronize with the periodic force if its intensity is sufficiently large. Our goal is to understand the conditions for global synchronization as a function of the fraction of nodes being forced and how these conditions depend on network topology, strength of internal couplings and intensity of external forcing. Numerical simulations show that the force required to synchronize the network with the external drive increases as the inverse of the fraction of forced nodes. However, for a given coupling strength, synchronization does not occur below a critical fraction, no matter how large is the force. Network topology and properties of the forced nodes also affect the critical force for synchronization. We develop analytical calculations for the critical force for synchronization as a function of the fraction of forced oscillators and for the critical fraction as a function of coupling strength. We also describe the transition from synchronization with the external drive to spontaneous synchronization. Highlights • We study the partially forced Kuramoto model on networks. • The force for global synchronization scales with the inverse of the forced fraction. • Topology of the network and the set of forced nodes influences global synchronization. • Synchronization with a given fraction requires a minimum internal coupling strength. • We present numerical and analytical calculations. [ABSTRACT FROM AUTHOR]

Abstract In previous studies, the heterogeneity of complex networks has been extensively studied. In our study, the heterogeneity of distance matrices is studied based on the Rényi index of networks. We define a new metric and name it global Rényi index (G R I), and prove several properties. In particular, the G R I value of the distance matrix corresponding to the evenly distributed point set in the Euclidean space is zero. Some model data were used to clarify the geometric meanings of G R I , and then we studied the G R I value of financial data. The results show that the G R I value in the real market changes drastically and is significantly different from the G R I value of the model-generated data. These results suggest that the proposed concept (G R I) is meaningful to the study distance matrix and provides a new perspective based on the network. Highlights • An index for characterizing the heterogeneity of the distance matrix is proposed. • We used GRI to analyze the data generated by some models and clarify the geometric meaning of GRI. • Research shows that large market fluctuations can significantly affect the heterogeneity of the distance matrix. • There are some significant differences between the GRI values of the Chinese market and the US market. [ABSTRACT FROM AUTHOR]

Abstract Multiplex networks, where nodes may interact through several different types of links, have been used widely to describe various complex systems. Many complex systems have an important feature that pairs of nodes may be connected by different types of links at the same time. In order to study the effect of the overlapped links systematically, in this paper I propose an algorithm to generate general multiplex networks with overlapped links. This algorithm could construct multiplex networks with arbitrary degree–degree distribution in each layer, and more importantly, could further adjust the proportion of overlapped links over a wide range. Therefore, the algorithm proposed in this paper offers an approach to construct multiplex networks into a delicate extent, which may provide a formal setting to study the topological impact of multiplex networks in a systematic manner. Highlights • I introduce a modified algorithm to generate a multiplex networks based on overlapped links. • I maximize the average degree of the multiplex network both in theoretical and numerical ways. • The results manifest the satisfactory performance of the algorithm with the theoretical analysis. • The proposed algorithm and given conditions could generate multiplex networks in a rather general extent. [ABSTRACT FROM AUTHOR]

Abstract With the hyperbolic model of the hidden metric space of networks, the hyperbolic DC of a node is defined, totally based on node features in the hyperbolic space but not directly related to network structures. The effectiveness of the hyperbolic DC in forecasting the true importance ranking of nodes in the network structure is studied. Simulations on the forecasting accuracy show it has a certain effectiveness in forecasting a few of the most important nodes, which provides possibility to carry out targeted attacks on networks without knowing any information of network structures. Moreover, for random attacks, a mechanism based on the hyperbolic DC is designed to enhance the destructive power, and the macro-matching degree is proposed to measure the effectiveness of the mechanism. Simulations show when parameter β is not big, the mechanism has quite good performance, and the smaller the value of β , the more effective the mechanism. Furthermore, for parameters in the hyperbolic model, their influences on the mechanism are researched. Results show temperature has more obvious influences than curvature, and the mechanism is found to become more effective when temperature becomes lower. According to the relationship between temperature and the clustering feature of the network, the research indicates our mechanism for random attacks should be effective for most real-world networks. Highlights • A new kind of node centrality not directly related with network structures is defined. • A mechanism to study targeted attacks without knowing network structures is proposed. • A mechanism to greatly enhance the destructiveness of random attacks is proposed. • Influences of the parameters of the hyperbolic model on the mechanism is studied. [ABSTRACT FROM AUTHOR]

Abstract Long-range bidirectional transport depends on the collective motion of interacting particles and associations in the entrance and exit of several transport mediums. Motivated by these observations we analyze a bidirectional framework with a narrow entrance to explore the fundamental role of interactions on the system dynamics. The complex phenomena such as phase transitions and spontaneous symmetry breaking for variant attractive and repulsive interactions are examined within the structure of the mean-field theory and computer simulations. We examined the control of interactions on the symmetry breaking structure, in particular, its appearance/disappearance. It is observed that for a certain range of attraction and repulsion rates, the system exhibits both symmetric and asymmetric behavior, while for their higher values asymmetry disappears. The critical values of interaction strength, beyond which symmetrical and asymmetrical phases appear and disappear are identified and the phase transition lines for these phases have been obtained theoretically. Interestingly, it is found that the region of asymmetric phases exhibits non-monotonic behavior with increasing attractive interactions. The finite size effect and symmetry-breaking phenomenon based on the density distribution functions from Monte Carlo simulations have also been examined. Finally, the impact of correlation strength on the system behavior has been analyzed using two-point correlation function based on simulations. Highlights • We formulate a bidirectional TASEP model considering narrow entrances and interacting particles. • The phase diagrams are obtained using mean-field theory and Monte Carlo simulations. • We examine the control of interactions on the symmetry breaking structure. • The phase properties with strong size dependency is observed. • Significant impact of correlation strength on system behavior is indicated. [ABSTRACT FROM AUTHOR]

Abstract Recently, network vulnerability or security has attracted much attention in various networked systems, and especially in security related attacks or protections, there are a set of influential nodes that can remarkably break the network connectivity. In this work, we firstly present eight attack mechanisms including target attack, random failure, betweenness based attack, closeness based attack, PageRank based attack, k-shell based attack, greedy algorithm, and low-degree attack. Secondly, inspired by the dynamic node removal process, we propose to recalculate the metrics for every node removal strategy, and evaluate the network robustness against all these heuristic attack strategies with and without recalculations in scale-free networks, random networks, and many real network models. The simulations indicate that most of the attack strategies with recalculations appear to imperil the network structure security more. Furthermore, considering that key node set mining is very critical for network structure protections, we employ minimum number of key nodes (MNKN) metric to further discuss the network vulnerability against all the attack strategies with or without recalculations. The results show that the critical nodes' group can be more efficiently found under the PageRank based attack with recalculations than under other attack disciplines with or without recalculations in most of the classic and real network models. This work investigates network structure vulnerability and security from a new perspective, and has potential applications into network structure protection or planning. Highlights • Network robustness is evaluated under various attack strategies. • Critical nodes' group mining problem is proposed and extensively discussed. • Potential applications are possible in network security area. [ABSTRACT FROM AUTHOR]

Abstract Strategy preference is a nonnegligible factor in the evolution of cooperation. In the present paper, we propose a modified strategy updating model, in which strategy preference has a significant influence on the strategy adoption. Strategy preference adjusts the probability of imitation according to partner's strategy in the spatial prisoner's dilemma game. This characteristic depicts the subjectively cognitive ability of players in decision making. To explore the effect of the newly introduced mechanism, we also control the proportion of players using this new model. Numerical results chiefly focus on the evolution and proportion of cooperation, which are obtained under different proportions of this mechanism and intensities of strategy preference. For this dynamics, the results show that the fraction of cooperators presents a growth trend within a wide range of parameters. To validate the generality of this mechanism, we also employ the snowdrift game. Moreover, the evolution of cooperation on Erdős–Rényi random graphs is studied for the prisoner's dilemma game. Interestingly, our analysis also reveals that the one's strategy preference can influence the strategy selection of others. The results are conducive to understanding the emergence and persistence of cooperation during the strategy adoption in reality. Highlights • A new strategy updating rule based on strategy preference is proposed. • Strategy preference can improve the individual readiness for cooperation. • The fraction of cooperators is enhanced effectively compared with the classical case. [ABSTRACT FROM AUTHOR]

Abstract Community detection is an important problem of complex networks analysis and various methods have been proposed to solve it. However, most of the existing methods only use the link information. As a result, the quality of their detected communities is often poor due to the sparse and noisy data existing in link information. Actually, content information of complex networks can also help to improve the quality of community detection. In this paper, we propose a method based on Multi-View Clustering via Robust Nonnegative Matrix Factorization (MVCRNMF). This method can provide a unified framework to combine link and content information for community detection. Its key idea is to build a multi-view robust NMF model with the co-regularized constraint on community indicator matrices of link view and content view. This can make link and content information complement each other during the factorization process of NMF. We devise iterative update rules as the optimization solution to the community detection model and also give the rigorous convergence proof. It is worth noting that MVCRNMF can learn the contribution weights from link and content information adaptively and this helps to save a lot of time on tuning the weight parameters. We conduct comparative experiments on four real-world complex networks. The results demonstrate that MVCRNMF performs better than state-of-the-art methods. Additionally, results of the case study on a co-authorship network also show that MVCRNMF can obtain higher quality communities. Highlights • A community detection model based on multi-view clustering is proposed. • We devise and prove iterative update rules for the community detection model. • The weight parameters of the community detection model can be learned adaptively. [ABSTRACT FROM AUTHOR]

Abstract In order to study the influence of the comment mechanism and the heterogeneity of underlying networks on the spreading of online social information, we present a new ICST (ignoramus-commentator-sharer-stifler) online social information spreading model based on scale-free networks. By using the mean-field theory, the spreading dynamics of the model is analyzed in detail. Then, the basic reproductive number R 0 and equilibriums are derived. Theoretical results show that the basic reproduction number is significantly dependent on the topology of the underlying networks. The relationships among the basic reproduction number R 0 , sharing rate, effective comment rate are studied. Furthermore, the global stability of the information-elimination equilibrium, the permanence of online social information spreading and the global attractivity of information-prevailing equilibrium are proved in detail. In addition, we study the influence of weight in networks and analyze the corresponding of dynamics behaviors. The adaptive weight cannot change the basic reproductive number, but it can weaken the information spreading. Numerical simulations confirmed the analytical results. Highlights • Present a new ICST online social information spreading model on scale-free networks. • Study the stability of equilibriums and the permanence of information spreading. • Comment mechanism and effective comment rate can affect information spreading. • Adaptive weight can affect the information spreading and the sustained level. [ABSTRACT FROM AUTHOR]

Abstract Identifying influential stocks and determining the diffusion mechanism in a complex stock network are helpful for recognizing and avoiding the risk of a financial crisis. We define the influence of a stock according to the price fluctuation that it triggers in other stocks. Meanwhile, we propose the fluctuation conduction network (FCN) model, a novel dynamic model that can provide an econometrics basis. Through the data of the closing price, we analyze the price fluctuation influence capacity (PFIC) of stocks. First, we test the validity of our method and compare the PFIC of stocks with other features of stocks; second, we rank the stocks and make an empirical analysis of the influential stocks. From this, we find the following: (1) The closeness centrality has a tight correlation with the PFIC. (2) From the individual stock level, the 10 most influential stocks in the giant component of the stock network have strong leadership and reputations in China. (3) From the industry sector level, "Finance" and "Electric, thermal, gas, water production and supply" are the most influential sectors. (4) Most stocks reach their maximum influence range at step 3 of the price conduction. Highlights • A method to quantify the influence capacity of stock price fluctuations is provided. • The influential stocks and influential industrial sectors are identified and analyzed. • The price conduction mechanism of stocks are analyzed. • The results of validity test and statistical analysis indicate our method performs well and efficiently. [ABSTRACT FROM AUTHOR]

Abstract Considering the differences of individuals' activities and personal propagation ability in real life, a new type of epidemic propagation model in activity-driven network is proposed. By employing the mean-field theory, we theoretically derive the epidemic spreading threshold, and using numerical simulation methods to further study our model in cases where the individual's activity rate is not related to propagation ability or related conditions. The results indicate that, when individuals' activity rate has no correlation to personal propagation ability, the epidemic threshold has a wide-ranging with the change of the average activity rate and the average propagation ability. When the recovery rate is small, individuals' average activity rate has less effect on the density of infected. If an individual's activity rate negatively correlates to his propagation ability, there is no significant correction between propagation ability and personal infected frequency. However, when the individual's activity rate and propagation ability are positively correlated, propagation ability and personal infected frequency show a strong positive correlation. Our research provide a feasible method to explore how the differences of individuals' activities and personal propagation ability affect the epidemic spreading. Highlights • We establish an epidemic propagation model in activity-driven network by considering the personal propagation ability. • The epidemic threshold is calculated theoretically using the mean field methods. • Our research provide a feasible method to explore how the differences of individuals' activities and personal propagation ability affect the epidemic spreading. [ABSTRACT FROM AUTHOR]

Abstract In recent research, the importance of determining social network users' spreading influence and ranking them has attracted plenty of attention. H-index is one of the methods that have been presented for this purpose, and determines the spreading capability of a node based on the degrees of its neighbors. In this method, part of the information on the neighbors is disregarded, which reduces ranking accuracy. In this paper, a measure is presented for specification of the centrality of nodes through extension of the H-index notion. The results of experimentation over real-world and artificial datasets demonstrate that the proposed measure exhibits higher accuracy and efficiency than in the other compared methods. Highlights • The goal is to rank influential users in social networks for spreading process. • We proposed a cumulative function for specification of nodes' neighbors information. • An extension of the H-index method is proposed based on the cumulative function. • The proposed method can rank influential users accurately. [ABSTRACT FROM AUTHOR]

Abstract The concept of networks in the context of graph theory delineates a wide variety of real-life complex systems. The theory of networks finds its applications very useful in many scientific and intellectual domains. Weighted networks can characterize complex statistical graph properties, particularly where node connections are heterogeneous. A framework of fuzzy weighted recurrence networks of time series is presented in this letter. Popular graph measures including the average clustering coefficient and characteristic path length of fuzzy weighted recurrence networks are shown to be more robust than those of unweighted recurrence networks derived from binary recurrence plots. Highlights • Applications of network theory are wide-ranging. • Weighted networks are ubiquitous in many real-life complex systems. • New concept of fuzzy weighted recurrence networks is presented. • Fuzzy weighted recurrence networks are more robust than unweighted recurrence networks. [ABSTRACT FROM AUTHOR]

Abstract The contagion law of credit risk is very important for financial market supervision. In the financial market, the interaction between credit risk holders’ sentiment and credit risk is the important factor of credit risk contagion. The existing credit risk contagion models based on complex network theory assume that the credit risk contagion is exogenous, and only analyze the proportion of the individuals infected by the credit risk from a macro perspective. However, existing models cannot explain the coupling relationship between credit risk contagion and emotional contagion, and how individuals are infected from a microscopic perspective is not clear. In this work, the theory of propagation dynamics in complex networks is introduced into the study of coupling relationship between credit risk contagion and sentiment contagion, which can reflect the endogenous problem of credit default in the process of credit risk contagion. The model can analyze the evolution process of individual risk contagion and sentiment contagion in the network, and can effectively reflect the risk contagion degree of individual. The proposed model further analyzes the influence of network structure, individual risk attitude, individual risk resisting ability, and financial market supervision on credit risk contagion and sentiment contagion. The correctness of the model is verified by theoretical deduction and numerical simulation. Highlights • Relationship between credit risk contagion and sentiment contagion is studied. • The proposed model can reflect the degree of individual infection. • The risk attitude, risk resisting ability, and supervision behavior are considered. • Analysis of different network structures is provided. [ABSTRACT FROM AUTHOR]

One of the main challenges to solve in an Industry 4.0 context for manufacturing leaders worldwide is increasing product and value-stream complexity. In this paper we present a standardized visualization methodology through multichannel plots applied to complex organizational design network configurations. Combining network theory and strategic lean management oriented organizational design through Hoshin Kanri Forest technology, this approach is expected to allow scholars and organizational leaders to systematically visualize strategic organizational design KPI (key performance indicator) dynamic states and hence significantly reduce KPI interpretation complexity. An example is briefly shown for explanatory purposes. [ABSTRACT FROM AUTHOR]

Highlights • A novel event-based intermittent control algorithm is given. • The synchronization criteria are acquired by theoretical analyses. • Achieve asynchronous sampling and control. Abstract In this paper, an intermittent control scheme based on double event-driven is proposed to solve the leader-following synchronization of complex networks (CNs). In the method, the control intervals for CNs are determined by two event conditions. In addition, the continuous communication among neighbor nodes is also avoided as an event occurring only depends on the last sampling information from one’s neighbors. Then, compared with existing results, the control scheme is more flexible and helpful for getting rid of the risk of CNs overload. Based on event-driven control approach and stability theory, several synchronization criteria for linear and nonlinear CNs are derived. Also, the rest interval upper bounds for asymptotic and exponential convergence case are obtained as well. Moreover, a synchronization algorithm for CNs via the intermittent control scheme with double event-driven is presented. Finally, two simulation examples are given to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Planning for nature conservation has increasingly emphasised the concepts of resilience and spatial networks. Although the importance of habitat networks for individual species is clear, their significance for long‐term ecological resilience and multi‐species conservation strategies is less established.Referencing spatial network theory, we describe the conceptual basis for defining and assessing a network of wildlife areas that supports species' resilience to multiple forms of perturbations and pressures. We explore actions that could enhance network resilience at a range of scales, based on ecological principles, with reference to four well‐established strategies for intervention in a spatial network ("Better, Bigger, More and Joined") from the influential Making Space for Nature report by Lawton et al. ().Building existing theory into useable and scalable approaches applicable to large numbers of species is challenging but tractable. We illustrate the policy context, describe the elements of a long‐term adaptive management plan and provide example actions, metrics and targets for early implementation using England as a case study, where there is an opportunity to include large‐scale ecological planning in a newly launched 25‐year environment plan.Policy implications. The concept of resilient ecological networks has attracted scientific and political support, but there is no consensus on what a resilient network would look like, or how to assess it. Therefore, it is unclear whether existing targets for action will be sufficient to achieve network resilience. We show that the scientific principles to place resilience and network theory at the heart of large‐scale and long‐term environmental planning are established and ready to implement in practice. Delivering a resilient network to support nature recovery is achievable and can be integrated with ongoing conservation actions and targets, by assessing their effectiveness on properties of the entire network. England's 25 Year Environment Plan promises to deliver a natural environment that is protected and enhanced for the future and so provides the ideal testbed. [ABSTRACT FROM AUTHOR]

The aim of this study is to contribute to the international business field and research on the innovativeness of international SMEs, by addressing the following research question: To what extent and under which circumstances does relationship connectedness affect perceived innovativeness in international SMEs? To answer this question, the authors create a model that examines the mediating effects of two types of foreign business relationship strategies: relationship differentiation and relationship investments. In order to create this model, the authors draw upon behavioural internationalization process theory, network theory and research into SMEs. The findings reveal that relationship investments mediate the effect relationship connectedness has on perceived innovativeness. This study contributes to research about the innovativeness of international SMEs by showing that being connected to resources in the network is not, in itself, a guarantee of becoming or remaining innovative in foreign markets. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the bus transport network (BTN) structure considering the spatial embedding of the network for three cities, namely, Hong Kong (HK), London (LD), and Bengaluru (BL). We propose a novel approach called supernode graph structuring for modeling the bus transport network. A static demand estimation procedure is proposed to assign the node weights by considering the points of interests (POIs) and the population distribution in the city over various localized zones. In addition, the end-to-end delay is proposed as a parameter to measure the topological efficiency of the bus networks instead of the shortest distance measure used in previous works. With the aid of supernode graph representation, important network parameters are analyzed for the directed, weighted and geo-referenced bus transport networks. It is observed that the supernode concept has significant advantage in analyzing the inherent topological behavior. For instance, the scale-free and small-world behavior becomes evident with supernode representation as compared to conventional or regular graph representation for the Hong Kong network. Significant improvement in clustering, reduction in path length, and increase in centrality values are observed in all the three networks with supernode representation. The correlation between topologically central nodes and the geographically central nodes reveals the interesting fact that the proposed static demand estimation method for assigning node weights aids in better identifying the geographically significant nodes in the network. The impact of these geographically significant nodes on the local traffic behavior is demonstrated by simulation using the SUMO (Simulation of Urban Mobility) tool which is also supported by real-world empirical data, and our results indicate that the traffic speed around a particular bus stop can reach a jammed state from a free flow state due to the presence of these geographically important nodes. A comparison of the simulation and the empirical data provides useful information on how bus operators can better plan their routes and deploy stops considering the geographically significant nodes. [ABSTRACT FROM AUTHOR]

Abstract: The network theory is widely applied to improve the reliability of a complex electromechanical system. In this application, system reliability assessment with network theory has been paid a great deal of attention. Because of instrument malfunctions, staff omissions, imperfect inspection strategies, and complex structures, field failure data are often subject to interval censoring, making the holistic reliability assessment becomes a difficult task. Most traditional methods assume reliability of critical components or partial reliability as system reliability, which may cause a large bias in system reliability estimation. This paper proposes a novel method to evaluate and predict the system reliability of a complex electromechanical system subject to the insufficient fault data problem from a network perspective. First, the system modeling based on network theory is developed to describe the topology of a holistic system. Second, interval‐valued intuitionistic hesitant fuzzy number is proposed in order to solve insufficient data for single component. Then, a new measure—comprehensive reliability—that can reflect the reliability of nodes in combination with functional properties and topological properties, which are formulated by failure data and network model, respectively, is constructed for system reliability assessment. Subsequently, an improved system reliability model based on percolation theory is given in terms of comprehensive reliability of nodes. Finally, to verify the effectiveness of the proposed method, a simulation and a real case study for traction system are implemented. [ABSTRACT FROM AUTHOR]

The Yaqui Valley territory in the northwest of Mexico was analyzed using networks theory that determines the flows density and actors centrality among the agricultural sector. These networks were those of public, financial, commercial, technical, and social policy management. The current research has been carried out with the general objective of identifying the actors that make up the networks in the Yaqui territory and their change in densities as one of the factors that determine the effectiveness of the policy applied in the territory, comparing two samples of 2006 and 2016. Linked to this objective, it is proposed to describe the obstacles for the densification of networks that favor the agricultural producers labor in the Yaqui Valley territory. The obtained results indicate a clear disarticulation in the different networks of study that can elicit serious social problems of inequality among the actors that are active in the agricultural sector. The networks found in the territory of the Yaqui Valley have been disarticulated over time, because ten years after being analyzed for the first time, the calculation of densities has been reduced by 24% on average, and the truss of the social sector practically tends to disappear. [ABSTRACT FROM AUTHOR]

This paper reviews recent research on the application of methods from the theory of networks for developing distributed control architectures for complex plants. The problem is defined as one of decomposing process networks into constituent subnetworks with strong intra-subnetwork and weak inter-subnetwork interactions. These interactions are quantified based on connectivity and response sensitivity information. This perspective is inspired by the community detection problem in networks. Several approaches are discussed based on hierarchical clustering and modularity optimization. The concepts and potential of these methods for developing control architectures for complex plants are illustrated through a case study. Future research directions are also discussed. [ABSTRACT FROM AUTHOR]

Earthquakes as spatio-temporal complex systems have been recently studied using complex network theory. Seismic networks are dynamical networks due to addition of new seismic events over time leading to establishing new nodes and links to the network. Here we have constructed Iran and Italy seismic networks based on Hybrid Model and testified the preferential attachment hypothesis for the connection of new nodes which states that it is more probable for newly added nodes to join the highly connected nodes comparing to the less connected ones. We showed that the preferential attachment is present in the case of earthquakes network and the attachment rate has a linear relationship with node degree. We have also found the seismic passive points, the most probable points to be influenced by other seismic places, using their preferential attachment values. [ABSTRACT FROM AUTHOR]

We study brain network data of three species, namely, C. elegans, cat and macaque monkey within the framework of network theory and Potts Hamiltonian model, and explore rich fractal nature in it, which could be an important signature of self-organization, and a simple rule to be obeyed in complex patterns of brain networks. Further, this fractal behaviors in topological parameters of brain networks at various network levels could be an indicator of systems level organization in complicated brain functionality. Again, Rich-club formation of leading hubs in brain networks becomes unpredictable as one goes down to different levels of organization. The popularity of these leading hubs in main modules or sub-modules also gets changed at different network levels, with varied attitudes at each level. Moreover, distribution of edges, which involves intra- and inter-modular/sub-modular interactions, inherited from one level of organization to another level follows fractal law. In addition to this, the Hamiltonian function at each network level, which may correspond to the energy cost in network organization at that level, shows fractal nature. Significant motifs, which are building blocks of networks and related to basic functionalities, in brain networks is found to be triangular motif, and its probability distribution at various levels as a function of size of modules or sub-modules follows fractal law. [ABSTRACT FROM AUTHOR]

Invulnerability is not the only factor of importance when considering complex networks’ security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair. [ABSTRACT FROM AUTHOR]

We present a two sided resource allocation problem, between demands and supplies, where both parties are ranked. For example, in Big Data problems where a set of different computational tasks is divided between a set of computers each with its own resources, or between employees and employers where both parties are ranked, the employees by their fitness and the employers by their package benefits. The allocation process can be viewed as a repeated game where in each iteration the strategy is decided by a meta-rule, based on the ranks of both parties and the results of the previous games. We show the existence of a phase transition between an absorbing state, where all demands are satisfied, and an active one where part of the demands are always left unsatisfied. The phase transition is governed by the ratio between supplies and demand. In a job allocation problem we find positive correlation between the rank of the workers and the rank of the factories; higher rank workers are usually allocated to higher ranked factories. These all suggest global emergent properties stemming from local variables. To demonstrate the global versus local relations, we introduce a local inertial force that increases the rank of employees in proportion to their persistence time in the same factory. We show that such a local force induces non trivial global effects, mostly to benefit the lower ranked employees. [ABSTRACT FROM AUTHOR]

Global risks interconnect with one another and threaten our society as a highly interdependent system. However, knowledge on how risk materializations influence one another is limited. Thus, this study provides a network model to measure risk interdependence and presents recommendations on the integrative risk management framework. This study develops a global risk network and calculates its key indicators of structural characteristics. Results show that global risks are closely interconnected and a particular triangle relationship exists among environmental, geopolitical, and societal risks. To improve the resilience of a risk system, the systematic important risks, which are “man-made environmental catastrophes” and “interstate conflict” in this study, should be monitored and controlled. Furthermore, we simplify the risk system by entirely managing risks in the same group based on our reclassification. Our method is considerably effective in detecting upcoming crisis and assists supervisors take timely action to prevent crisis. [ABSTRACT FROM PUBLISHER]

The article offers information on statistical models related to population of network in terms of replicated network data. It mentions the observations of the author related to sparse network and variational autoencoder approach.Topics include bayesian modelling, Gibbs sampler and stochastic gradient Markov chain Monte Carlo (MCMC).

The Internet forms a platform featured with high liquidity, accessibility and concealment for the public to express their respective views on certain events, thus leading to a large network graph. Due to such environmental features, the public opinions formed on the Internet are different from those on traditional media. Studies focusing on the former area are relatively fewer. In addition, the majority of existing methods proposed for constructing the Internet public opinion topological structure are based on the classic BA model, thus resulting in drawbacks in the range of simplicity and a lack of strict deduction. Therefore, based on the complex networks theory, a model applied to describe the topology of the Internet public opinion is deduced with rigorous derivation in the present paper. Results show that the proposed expression could well reflect the degree distribution of Internet public opinion which follows an analogous power law distribution, and that the peak value and the degree distribution are not correlative to each other. Moreover, it has been also proved that compared to the classic BA model, the proposed model has better accuracy performance in the description of the degree distribution of the Internet public opinion, which contributes to future studies focusing on this area. Thus, an attempt has been made to give the first theoretical description of the Internet public opinion topology in the present paper. In addition, it is also the first paper focusing on the solution of networks degree distribution with an exponential growth form. [ABSTRACT FROM AUTHOR]

The air transportation network across a certain area, a typical multilayer complex system, is composed of a number of individual airlines, each of which forms a single layer. A central problem in airline merging is the minimal number of layers, denoted by , that is needed for the merged network to function nearly as well as the fully aggregated network. For this purpose, we monitor several structural measures during the network aggregation process in which is gradually increased. For China Air Transportation Network (CATN), we identify a common, sharp transition point yielding . Whereas, for European Air Transportation Networks (EATN) there is no clear signal of such a transition point. The transition point of CATN is confirmed by means of the Wasserstein distance and the relative entropy. Our results indicate that there is still room in the optimization of CATN while for EATN, it seems better to keep the present structure. [ABSTRACT FROM AUTHOR]

Recently, a number of similarity-based methods have been proposed to predict the missing links in complex network. Among these indices, the resource allocation index performs very well with lower time complexity. However, it ignores potential resources transferred by local paths between two endpoints. Motivated by the resource exchange taking places between endpoints, an extended resource allocation index is proposed. Empirical study on twelve real networks and three synthetic dynamic networks has shown that the index we proposed can achieve a good performance, compared with eight mainstream baselines. [ABSTRACT FROM AUTHOR]

In pinning control of complex networks, it is found that, with the same pinning effort, the network can be better controlled by pinning the large-degree nodes. But in the clustered complex networks, this preferential pinning (PP) strategy is losing its effectiveness. In this paper, we demonstrate that in the clustered complex networks, especially when the clusters have different size, the random pinning (RP) strategy performs much better than the PP strategy. Then, we propose a new pinning strategy based on cluster degree. It is revealed that the new cluster pinning strategy behaves better than RP strategy when there are only a smaller number of pinning nodes. The mechanism is studied by using eigenvalue and eigenvector analysis, and the simulations of coupled chaotic oscillators are given to verify the theoretical results. These findings could be beneficial for the design of control schemes in some practical systems. [ABSTRACT FROM AUTHOR]

Based on the density of connections between the nodes of high degree, we introduce two bounds of the spectral radius. We use these bounds to split a network into two sets, one of these sets contains the high degree nodes, we refer to this set as the spectral-core. The degree of the nodes of the subnetwork formed by the spectral-core can give an approximation to the top entries of the leading eigenvector of the network. We also present some numerical examples showing the dependancy of the spectral-core with the assortativity coefficient, its evaluation in several real networks and how the properties of the spectral-core can be used to reduce the spectral radius. [ABSTRACT FROM AUTHOR]

Seismic hazard evaluation before recent strong main shocks in the area of Greece is attempted using prior seismicity on the basis of earthquake network theory. The connections of earthquake networks are constructed from successive earthquakes and the nodes are represented by cells of normal grids that were considered superimposed on the study areas. The dynamic evolution of the network structure is examined at sliding windows for identifying periods of statistically significant change, i.e., the network structure differentiation from that of a random network, where the structure is characterized by selected network measures, including the index of small-worldness property. By studying the structure of complex earthquake network, a distinct dynamic evolution is revealed, 2 months before the main shock occurrence. Particularly, the network measures, such as clustering coefficient and small-worldness index, tend to increase before and exhibit an abrupt jump at the time of the main shock occurrence, and then slowly decrease and become stable with small variations as before. [ABSTRACT FROM AUTHOR]

Background: Global health partnerships have grown rapidly in number and scope, yet there has been less emphasis on their evaluation. Gavi, the Vaccine Alliance, is one such public-private partnership; in Gavi-eligible countries partnerships are dynamic networks of immunization actors who work together to support all stages and aspects of Gavi support. This paper describes a conceptual framework - the partnership framework - and analytic approach for evaluating the perceptions of partnerships' added value as well as the results from an application to one case in Uganda. Methods: We used a mixed-methods case study design embedded in the Gavi Full Country Evaluations (FCE) to test the partnership framework on Uganda's human papillomavirus (HPV) vaccine application partnership. Data from document review, interviews, and social network surveys enabled the testing of the relationships between partnership framework domains (context, structure, practices, performance, and outcomes). Topic guides were based on the framework domains and network surveys identified working together relationships, professional trust, and perceptions of the effectiveness, efficiency, and legitimacy of the partnership's role in this process. Results: Data from seven in-depth interviews, 11 network surveys and document review were analyzed according to the partnership framework, confirming relationships between the framework domains. Trust was an important contributor to the perceived effectiveness of the process. The network was structured around the EPI program, who was considered the leader of this process. While the structure and composition of the network was largely viewed as supporting an effective and legitimate process, the absence of the Ministry of Education (MoE) may have had downstream consequences if this study's results had not been shared with the Ministry of Health (MoH) and acted upon. The partnership was not perceived to have increased the efficiency of the process, perhaps as a result of unclear or absent guidelines around roles and responsibilities. Conclusion: The health and functioning of global health partnerships can be evaluated using the framework and approach presented here. Network theory and methods added value to the conceptual and analytic processes and we recommend applying this approach to other global health partnerships to ensure that they are meeting the complex challenges they were designed to address. [ABSTRACT FROM AUTHOR]

The Ryu-Takayanagi (RT) formula relates the entanglement entropy of a region in a holographic theory to the area of a corresponding bulk minimal surface. Using the max flow-min cut principle, a theorem from network theory, we rewrite the RT formula in a way that does not make reference to the minimal surface. Instead, we invoke the notion of a 'flow', defined as a divergenceless norm-bounded vector field, or equivalently a set of Planck-thickness 'bit threads'. The entanglement entropy of a boundary region is given by the maximum flux out of it of any flow, or equivalently the maximum number of bit threads that can emanate from it. The threads thus represent entanglement between points on the boundary, and naturally implement the holographic principle. As we explain, this new picture clarifies several conceptual puzzles surrounding the RT formula. We give flow-based proofs of strong subadditivity and related properties; unlike the ones based on minimal surfaces, these proofs correspond in a transparent manner to the properties' information-theoretic meanings. We also briefly discuss certain technical advantages that the flows offer over minimal surfaces. In a mathematical appendix, we review the max flow-min cut theorem on networks and on Riemannian manifolds, and prove in the network case that the set of max flows varies Lipshitz continuously in the network parameters. [ABSTRACT FROM AUTHOR]

Efficient complex systems have a modular structure, but modularity does not guarantee robustness, because efficiency also requires an ingenious interplay of the interacting modular components. The human brain is the elemental paradigm of an efficient robust modular system interconnected as a network of networks (NoN). Understanding the emergence of robustness in such modular architectures from the interconnections of its parts is a longstanding challenge that has concerned many scientists. Current models of dependencies in NoN inspired by the power grid express interactions among modules with fragile couplings that amplify even small shocks, thus preventing functionality. Therefore, we introduce a model of NoN to shape the pattern of brain activations to form a modular environment that is robust. The model predicts the map of neural collective influencers (NCIs) in the brain, through the optimization of the influence of the minimal set of essential nodes responsible for broadcasting information to the whole-brain NoN. Our results suggest intervention protocols to control brain activity by targeting influential neural nodes predicted by network theory. [ABSTRACT FROM AUTHOR]

We propose two random network models for complex networks, which are treelike and always grow very fast. One is the uniform model and the other is the preferential attachment model, and both of them depends on a parameter 0

In this paper, complex network theory is used to make time-series analysis of key indicators of governance structure and financing data. We analyze scientific listed companies’ governance data from 2010 to 2014 and divide them into groups in accordance with the similarity they share. Then we select sample companies to analyze their financing data and explore the influence of governance structure on financing decision and the financing preference they display. This paper reviews relevant laws and regulations of financing from the perspective of law and economics, then proposes reasonable suggestions to consummate the law for the purpose of regulating listed companies’ financing. The research provides a reference for making qualitative analysis on companies’ financing. [ABSTRACT FROM AUTHOR]

A statistical-mechanical theory of thermoreversible gelation which is formed by monodisperse telechelic associating polymers with junction multiplicity of three is developed. In the present theory, the effect of loop formation is considered. Using the theory, the properties of the system are obtained, such as the state of association of polymers, the sol-gel transition line, and the number concentration of elastically effective chains. In addition, a Monte Carlo simulation of a bead-spring model of monodisperse telechelic associating polymers is performed. The simulation results are in good agreement with the present theoretical results. Furthermore, the shear modulus is calculated by an application of phantom network theory and compared with the experimental data. The theoretical results agree well with the experimental results. It is shown that the loop formation occurs especially in dilute regime and causes the decrease of the modulus in the regime. [ABSTRACT FROM AUTHOR]